The present disclosure generally relates to enhancement methods for Active Appearance Models (AAM), and more particularly to a multi-resolution AAM fitting method for low-resolution images.
Model-based image alignment is a fundamental problem in computer vision. Since the early 1990's, AAM have been a very popular method of image alignment. For facial images analysis, face alignment/fitting using AAM are receiving more attention among those skilled in the art of computer vision technology, because it enables facial feature detection and pose rectification. However, most of the existing work focuses on fitting AAM to high quality images. The heightened awareness of the need to monitor public spaces for terrorist and criminal activity has lead to a proliferation of surveillance cameras in public venues. Unfortunately, many of these cameras produce low-resolution images. Therefore, identifying individuals and objects using these low-resolution images can be difficult. To overcome this problem, there exists a need for a computer vision enhancement system that is capable of low-resolution image fitting and alignment using AAM. How to effectively fit AAM to low-resolution facial images is an important question.
There are two basic components in face alignment using AAM. One is face modeling and the other is model fitting. Given a set of facial images, face modeling is the procedure for training the AAM. AAM are essentially two distinct linear subspaces modeling the image shape and appearance separately. Once the AAM are trained, model fitting refers to the process of fitting the AAM to facial or other images so that the cost function measuring the distance between the image and AAM is minimized. In other words, fitting involves matching the AAM to a facial or other image.
Conventional face modeling directly utilizes the manual labeling of facial landmarks and uses them in training the shape model. However, manual labeling tends to have various errors, which affects the resultant shape model and as well the appearance model.
One requirement for AAM training is the manual labeling of facial landmarks for all training images. This is a time-consuming task that involves manual labeling of image landmarks for all training images. This is not only a time-consuming manual operation, but also a process prone to frequent errors. The frequency of errors may be due to a number of factors including the human factor involved. For example, the same person might have slightly different labeling for the same image when he or she labels it the second time. Also, different people have different labeling for the same image. Another factor is the inherent confusing definition of some landmarks. For example, there is no facial feature to rely on in labeling the landmarks along the outer-boundary of the cheek. Thus, it is hard to guarantee these landmarks correspond to the same physical position under multiple poses.
The error in labeling affects image modeling. In shape modeling, the resultant shape bias models not only the inherent shape variation, but also the error of the labeling, which is not desirable. In the appearance modeling, the appearance bias contains more low frequency information, which is an unfavorable property for model-based fitting.
Furthermore, in fitting AAM to low-resolution images, there is a potential mismatch between the model resolution and the image resolution. Therefore, there persists a need to fit AAM to low-resolution facial images.